As PredictLeads partnered up with clay.com, it seemed only natural to then go work with clay in the literal sense. This new phase marks the exciting PredictLeads Clay partnership, further enhancing collaboration.
As much as we try it’s really challenging for all of us to look at the camera at the same time đ
Jokes aside, it was an interesting coincidence though not planned to happen :). Eva booked us for a nice evening of working with clay (literally). Through this collaboration with clay, the PredictLeads Clay partnership certainly extended beyond just business.
Screenshot
When working with clay to create a finished ceramic product, two primary steps are needed when heating / firing the clay:
There were also two types of “tables” we were working at. This was the “Pottery Mill”.
Bisque Firing (First Firing): The purpose is to transform the raw clay into a hard ceramic state. This firing removes moisture from the clay and drives out organic materials. The temperature for bisque firing in our case was 950°C (1,750°F).
Glaze Firing (Second Firing): After bisque firing, the piece is coated with glaze, a glass-like coating that provides a smooth, glossy finish. The glazed piece is then subjected to a second firing, known as glaze firing. This firing melts the glaze and forms a glassy, non-porous surface on the ceramic piece. In our case Glaze firing temperature was 1,250°C (2,345°F).
Till the next team meetup! đ
We’re still waiting for the clay shop to go through these two steps and we can’t wait to see what we produced :). Ultimately, this hands-on experience is all part of what makes the PredictLeads Clay partnership such an enriching experience.
We are excited to announce a new API endpoint from PredictLeads designed to help you discover which companies are utilizing specific technologies. Whether you’re tracking the adoption of CRM systems, cloud computing platforms, enterprise resource planning tools and more, this API offers a powerful way to gather and analyze technology usage data across the web.
How It Works
Our new endpoint allows you to ping a specific Technology ID and receive a detailed list of companies and websites utilizing that technology. This data can be invaluable for market research, sales prospecting, competitive analysis and more.
Example API Endpoint
You can use the following endpoint to start exploring technology detections:
Additional information can be found in our docs âhereâ.
Interested in Trying It Out?
Weâre offering 100 free API calls to anyone who wants to test this new endpoint. Sign up at PredictLeads and start exploring + Feel free to let us know if there are any specific technologies or IDs you’d like to check the coverage of.
Note on Development
Please note that we are continually improving this endpoint, and your feedback is essential. If you encounter any issues or have suggestions, feel free to reach out to our support team.
Technology Data Snapshot
Technologies Tracked: ~15,000
Technology Adoptions Detected Since 2018: ~636 million
Websites Tracked: ~47 million
Technology Identifications Last Month: ~18 million
Technology Identifications Last Year: ~193 million
We look forward to seeing how you use this new feature to enhance your business intelligence and decision-making processes!
Hey everyone! Today, let’s dive into how personalized sales outreach with data can revolutionize your approach and make connections more meaningful.
In sales, finding and engaging the right prospects can feel like searching for a needle in a haystack. Sending non personalized emails is just a thing of the past and companies offering sales solutions are looking into data to add that personalized touch that increases those reply rates that we all like.
Job Openings Dataset as well as the News Events Dataset are incredibly useful and widely adopted for uncovering new leads and improving sales outreach. However, there is a unique dataset that is gaining significant attention. This dataset, which is not yet widely used due to its limited availability, holds great potential for transforming sales strategies. Here is why:
We all know that companies like to put logos of other companies they work with, on their website to gain credibility. Since those logos are often not backlinked, PredictLeads has built an image recognition system that connects these logos with company domain names. By checking the companyâs Case studies pages, testimonials, “Our customers” sections and more allows PredictLeads systems to identify them as customers, partners, vendors, sponsors and more.
Hereâs a quick rundown of how the Connections Dataset can revolutionize your sales efforts and how itâs used to target High-Value Prospects.
Identifying and Prioritizing Key Prospects
First up, letâs talk about finding those high-value prospects. With the Connections Dataset, you can pinpoint companies that already have significant relationships with your existing clients or partners. This means theyâre more likely to convert because thereâs already some trust and relevance built in.
How to Do It:
Analyze Data: Dive into the Connections Dataset to find companies that share multiple connections with your current network.
Prioritize Prospects: Rank these companies based on the number and quality of shared connections.
Sales Outreach: Focus your efforts on these high-value prospects. Make sure to highlight the mutual connections and the benefits of joining an established network.
Example: A SaaS company finds that several of its clients are partners with a leading industry player. By targeting this player and emphasizing the mutual benefits, they can craft a top notch outreach thatâs hard to ignore.
Next, letâs make your emails shine.
Personalized outreach campaigns are the way to go because they address the specific needs of each recipient. By referencing the target companyâs partnerships or integrations, your emails can be way more relevant and engaging.
How to Do It:
Gather Insights: Use the Connections Dataset to get detailed insights into the target companyâs partnerships and integrations.
Personalize Emails: Craft email content that references these relationships, making it super relevant.
Automate Personalization: Use AI tools to scale this personalization process, ensuring each email is tailored to the recipientâs context.
Example: An AI-powered email platform identifies a potential clientâs recent partnership with an e-commerce platform like Shopify. They send a personalized email campaign highlighting success stories of similar clients who benefited from this integration. Boom -> relevance and appeal.
Warm Introductions through Mutual Connections
Finally, letâs talk about using mutual connections for warm introductions. These can significantly boost your chances of successful engagement. The Connections Dataset can help you leverage existing relationships to approach leads with more trust and credibility.
How to Do It:
Map Networks: Use the ConnectionsDataset to map out mutual connections between your company and target leads.
Request Introductions: Reach out to these mutual connections for warm introductions, explaining the mutual benefits.
Follow-Up Strategy: Develop a follow-up strategy that leverages the credibility of the mutual connection.
Example: A lead generation company finds that one of its key clients is also a partner of a high-value prospect. They request an introduction from the key client, who provides a warm referral, significantly improving engagement chances and improves their data-driven sales outreach.
Utilizing AI for Enhanced Personalization & amplify the Impact with automation
AI can take your use of the ConnectionsDataset to the next level by automating the analysis and personalization processes. Here are some tips:
Automated Analysis: AI analyzes the dataset to identify patterns and insights, like high-value prospects and mutual connections.
Scale Personalization: AI personalizes email content at scale by incorporating insights from the dataset into email templates.
Predictive Analytics: AI uses historical data to predict which prospects are most likely to convert, helping prioritize efforts.
Continuous Learning: AI systems learn from campaign outcomes, refining algorithms to improve future personalization and targeting.
Example Implementation:
An AI-powered email platform integrates with the Connections Dataset, analyzing the dataset to identify key relationships and generating personalized email content. It predicts which prospects will respond positively and continuously refines its personalization algorithms.
Conclusion
Since 2019, over 170 million business connections have been detected, with business connections data available for 38,5 million websites. Last month alone, there were approximately 12 million business connections, and around 57 million over the past year. The Connections Dataset is a goldmine for lead generation companies and those using AI for personalized emails. By providing detailed insights into company relationships, it helps you target high-value prospects, create relevant and engaging email campaigns, and leverage mutual connections for credible engagements. Combined with AI, it automates these processes and achieves personalization at scale, leading to higher engagement rates and better sales outcomes.
Feel free to let us know if you or if you’d like to learn more. Weâre here to help:)!
Artificial intelligence is changing the job market, prompting significant shifts in workforce needs across various sectors. By analyzing job postings, investment companies can gain insights into which industries are reducing their hiring for roles likely to be automated. This helps them understand potential revenue impacts and growth opportunities.
Detecting AI Adoption Trends
AI tools are increasingly integrated into business functions, ranging from data analysis to customer service and legal assistance. For example, paralegals, traditionally performing research and document review, are being replaced by AI systems. These systems can quickly and accurately handle these tasks. This trend is highlighted in Nexford University’s article “How Will Artificial Intelligence Affect Jobs 2024-2030,” which underscores the growing use of AI in roles previously performed by humans. Monitoring job postings can reveal decreases in hiring for such roles, indicating a shift towards AI-driven solutions.
Strategic Insights for Investment
Investment companies must stay ahead of market changes to make informed decisions. A decline in job openings for traditional roles, such as customer service representatives or paralegals, in sectors like customer service, sales, and legal services can signal a move towards AI automation. This information is crucial for identifying industries at risk of revenue loss due to a lack of automation foresight. It helps investors focus on more promising areas.
For example, companies like Google and Duolingo are already replacing human roles with AI technologies. Google has integrated AI into its customer care and ad sales processes. Meanwhile, Duolingo uses AI for content translation, reducing the need for human contractors.
Economic Impact of AI
The economic implications of AI are substantial. A McKinsey report predicts that AI could add $13 trillion to global economic activity by 2030, primarily through labor substitution and increased innovation. However, this growth comes with job displacement. Monitoring job opening trends helps investment firms gauge which companies and sectors are reducing their workforce due to AI, identifying potential risks and opportunities.
Understanding AI adoption through job postings allows investment companies to anticipate market shifts. They can focus on high-growth sectors. Sectors such as AI development, advanced manufacturing, and healthcare innovation are likely to attract more investment. This is due to their proactive adoption of AI technologies. This foresight helps investors mitigate risks and capitalize on new growth opportunities.
Additional Data from the ADP National Employment Report
The ADP National Employment Report for June 2024 provides a comprehensive overview of job trends. According to the report, private employers added 150,000 jobs in June, marking a slowdown in job creation for the third straight month. “Job growth has been solid, but not broad-based. Had it not been for a rebound in hiring in leisure and hospitality, June would have been a downbeat month,” said Nela Richardson, Chief Economist at ADPâ (ADP Media Center)â.
This data underscores the importance of monitoring employment trends to understand the broader economic impact of AI. It informs strategic investment decisions.
The chart titled “ADP Employment: Establishment Size Year-over-Year Percent Change” tracks the year-over-year percentage change in employment across different establishment sizes from 2011 to 2024.
Here are some key points:
Trend Analysis: The chart illustrates fluctuations in employment growth across different establishment sizes over the years. A notable drop is observed around 2020, corresponding with the COVID-19 pandemic’s impact on employment. Post-2020, there is a marked recovery, with larger establishments (500+ employees) showing a more robust recovery compared to smaller establishments.
Recent Trends: As of June 2024, the growth rates have stabilized. However, smaller establishments (1-19 employees) show slower growth compared to larger establishments. This indicates that larger companies are recovering and possibly investing more in automation and AI technologies. Meanwhile, smaller businesses are facing more challenges.
This chart helps visualize the employment dynamics and how different-sized businesses have been affected over the years. It provides valuable context for understanding the broader economic landscape and the impact of AI on employment.
For more detailed insights and statistics, the full ADP Employment Report is available here.
Conclusion
By analyzing job openings data, investment companies can gain valuable insights into AI adoption trends and their impact on various sectors. This approach helps identify industries reducing traditional roles due to AI. It enables better-informed investment decisions. Utilizing datasets like those from PredictLeads can provide the detailed, real-time insights needed to stay ahead of market shifts. This helps mitigate risks and seize growth opportunities in an AI-driven economy.
Job Openings Data: Since 2018, there have been 166 million job openings detected.
Data Availability: Job openings data is available for 1.6 million websites.
Recent Trends: Last month, there were 5 million job openings. Over the past year, approximately 50 million job openings were recorded globally.
Active Job Openings: Currently, there are about 7 million active job openings uncovered by PredictLeads.
These statistics underscore the vast amount of data available to track AI adoption and its effects on the job market. They provide investment firms with the necessary tools to make informed decisions.
Navigating the sales landscape with data isn’t just about collecting information – Itâs about turning it into actionable insights. This is exactly what Blueprint has mastered using PredictLeads.
đ€Letâs dive into how they do it. đ€
Data at Work: Real Insights, Real Growth
Blueprint uses PredictLeads to perform deep technographic scoring, analyzing data on 500 new technologies each week. This isn’t just about knowing whatâs out there -> it’s about predicting market trends and identifying emerging competitors, providing a clear advantage in crafting timely and relevant sales pitches.
Jordan Crawford, the founder of Blueprint, puts it simply: âItâs not about having more data, but about having the right data that you can actually use.â This is where PredictLeads shines, offering depth with actionable insights.
Key Stats and Strategic Decisions
With PredictLeads, Blueprint isnât just collecting data -theyâre strategically deploying it.
Hereâs how:
Job Openings Data: By analyzing the hiring trends of potential clients, Blueprint can pinpoint when companies are expanding and tailor their pitches to meet these growth phases.
Technology Adoptions: Tracking 636 million technology adoptions helps Blueprint stay ahead, suggesting when companies are likely to need their cutting-edge solutions.
A Relationship Built on Success
Blueprintâs partnership with PredictLeads goes beyond data. Itâs about continuous support and collaboration, which Crawford describes as unparalleled. âPredictLeads always finds a way to make it happen,â he says, emphasizing the personalized support that helps Blueprint leverage data effectively.
For those eager to dive deeper into this use-case, you can check it out here.
If you have any questions, please don’t hesitate to reach out to us at: info@predictleads.com
As global pension funds like CalPERS increasingly redirect investments from public equities to private markets, the demand for precise, actionable insights grows significantly. This strategic transition, aimed at securing higher yields and reducing market volatility, is particularly highlighted by pension funds’ systematic moves towards assets like private equity and private debt. This shift underscores the role of platforms such as PredictLeads in providing vital data insights in the realm of pension funds private markets.
The Role of PredictLeads’ Data in Navigating Private Markets
Job Openings Data:
PredictLeads’ Job Openings Data provides real-time insights into hiring trends directly from company websites, reflecting growth and expansion activities within specific sectors. An increase in recruitment, especially within sectors like private equity and private debt, often suggests robust sector health and promising profitability prospects. For pension funds diversifying their portfolios into these private markets, such insights are critical. They help align investment strategies with sectors that demonstrate strong growth potential, making this data invaluable for funds adjusting to the dynamic conditions of private markets. Additionally, pension funds can use this data to identify emerging industries or regions where new skills are in demand, providing an early indicator of economic shifts that could influence long-term investment decisions.
Business Connections dataset:
The Business Connections dataset from PredictLeads employs advanced image recognition to scan and categorize logos on company websites, unveiling key customer relationships often obscured in conventional financial reports. This dataset is especially valuable for pension funds engaging in scoring potential (startup) investments. Identifying major clients, particularly public companies, allows funds to assess a startup’s credibility and market traction. Companies with esteemed, stable clients can be scored higher, indicating lower risk and potentially higher reliability as investment opportunities. This insight is crucial for informed decision-making in public market investments.
Pension funds can leverage this data to assess the market reach and network strength of potential investments, ensuring a more comprehensive risk assessment and decision-making process. Furthermore, this dataset allows pension funds to monitor the customer base stability of current investments, providing ongoing risk management and insight into market position changes.
Broader Market Implications:
The shift of pension funds toward private markets not only reshapes their investment strategies but also influences broader market dynamics. By utilizing PredictLeads’ alternative data, such as Job Openings and Business Connections, pension funds can gain deeper insights into the risk and potential of their private market investments, which are crucial for informed decision-making in an environment where traditional metrics fall short.
Moreover, this strategic use of alternative data helps pension funds anticipate market trends, adapt to economic changes, and identify investment opportunities early, maintaining a competitive edge in an increasingly complex financial landscape. Whether through pinpointing emerging sectors with job openings data or evaluating the robustness of potential investments with key customer analysis, these datasets provide critical insights that enhance and refine investment strategies.
Conclusion:
As the trend of pension funds moving towards private markets continues to grow, the importance of alternative data becomes increasingly central. Datasets like PredictLeadsâ Job Openings and Business Connections dataset provide essential tools that enable large institutional investors to execute their strategies with greater precision and confidence. By leveraging these alternative data sources, pension funds can more effectively navigate the complexities of private markets, aligning their investment approaches with the most promising opportunities for sustainable returns.
See which companies are hiring for sales roles and might be interested in what you’re selling.
Use the data to make your marketing efforts more specific and effective.
Spot hiring trends that could lead to new opportunities for your business.
What can you expect in the shared file:
Job openings Title
Website domainÂ
Company TickerÂ
Companies Meta DescriptionÂ
& Much more
And if you’re really into data, we’ve got something special for you. We have a massive database with over 157 million job listings. You can dive into this data or use our API to get the insights you need directly.
We’re making a new list of job openings perfect for people at big companies like PwC, EY, KPMG, and Accenture. Want to find a great role? Let us know what you’re looking for.
Data analytics B2B marketing PredictLeads is reshaping how companies engage customers in todayâs digital landscape. With the rise of AI and big data, marketers no longer rely on guesswork â they leverage actionable insights from datasets like job openings to anticipate industry shifts, personalize campaigns, and drive higher engagement.
AI’s not just about making tasks easier – it’s about making marketing smarter! Picture this: AI dives into job opening data and picks up on which industries are booming and what skills are in demand. This goldmine of info helps marketers craft campaigns that hit RIGHT WHERE THEY NEED TO.
The real magic of AI in marketing? Personalization. AI spots patterns in how users behave and what they like, so messages can be tailored just for them. No more spammy, one-size-fits-all ads. It’s all about sending the right message, to the right person, at the right time.
Predictive analytics is another ace up AI’s sleeve. By looking at trends, like which job sectors are heating up, AI can predict where the market’s headed. This means businesses can adjust their strategies on the fly, staying ahead of the curve instead of playing catch-up.
But, it’s not all smooth sailing. With great data comes great responsibility. Issues like data privacy and ethical AI use are âkindaâ big. Plus, the success of AI-driven marketing hinges on the data’s quality.
In a nutshell, as AI tech evolves, its role in marketing only gets juicier. It’s all about digging into data-driven insights and riding the wave of personalized marketing. But, it’s crucial to play it smart and ethical. Get it right, and AI won’t just be a tool >> it’ll be your competitive edge in nailing customer engagement.<<
At the World Economic Forum’s Growth Summit, economist Richard Baldwin made a great point: “AI won’t take your job IF YOU KNOW HOW TO USE IT.” Add some Good Data into the equation, and you’re golden. đ„
Interested in seeing how PredictLeads’ Job Openings datasets can revolutionize your marketing and sales? We’d love to chat!
A HubSpot survey found that more than 40% of salespeople say prospecting is the most challenging part of the sales process and at least 50% of your prospects are not a good fit for what you sell. This is where data enrichment for sales prospects using PredictLeads can make all the difference. This is super frustrating!
*https://blog.hubspot.com/sales/sales-statistics
Outbound sales efforts are often tedious using up a lot of time and resources and often chasing the wrong types of prospects. CRMâs and sales platforms provide a lot of insights into a prospect but these are often irrelevant or out of date. The reason for this is that the data they utilize is not being updated often enough, only uses a couple of data sources or the platform doesnât have the capability to drill down enough or overlap the insights.
Thatâs why data driven sales teams are turning to data to enrich companies and help them filter and prioritize leads. Not only do they continue to use platforms like Salesloft, Outreach.io, HubSpot, Clearbit etc but they are taking it a few steps further and enriching their prospects even more. This gives them competitive advantage which helps them to increase reply rates and meetings with prospects.
Identify companies hiring
Finding which companies are hiring is a great signal because itâs likely that they are investing in people and resources. Sales teams use hiring data in two ways. A) to find companies with the most live jobs regardless of the job type of job or b.) finding companies hiring for particular roles. In the second instance, companies hiring for marketing are more likely to buy marketing automation and companies hiring for accounting are more likely to buy financial software.
Identify companies hiring for C level executives
Finding companies who are hiring for C level executives means that sales teams are more than a few steps ahead. This is because when a director, manager or head of a department joins a company, itâs likely that they will implement new changes, evaluate tools and resources and be open to change. Sales teams who look for these signals early secure meetings and get ahead of the line before their competitors, making this tactic a no brainer.
Resonating with a prospect
âHi John, Iâm reaching out because ⊠um ⊠because âŠâ. Sometimes finding a good prospect is easy but reaching out in a way that will grab the prospect’s attention is tiring, time consuming and frustrating. We all know that itâs important to resonate with a prospect so that they are more likely to open and read your email but finding that hook is like finding a needle in a haystack.
Data driven sales teams solve this by looking for newly available sales triggers like awards, new funding rounds, new partnerships, new integrations, hiring intent, companies they have in common with a prospect, latest acquisitions in their industry, new product offerings of their competitors etc. These are easy ways to create familiarity and show that you know something about their business.
Finding the Right Leads at Scale
Some sales reps cast their net too wide in an effort to attract as many prospects as possible and meet their quotas. Unfortunately, this often wastes time and creates low morale. Getting limited or no answers is frustrating and not a good feeling which ultimately reduces productivity.
To avoid this, it’s important to have the right data to quickly figure out which prospects to pursue. Having good data means a good lead list which means good quality emails, a high response rate and more meetings booked. More and more sales teams are using the help of growth experts or growth support teams to help them identify the right leads to keep on track. Growth teams then utilize data to gain sales triggers and build targeted lead lists which increases conversion rates.
PredictLeads data is one source of sales triggers, growth indicators and company intelligence which helps sales teams and sales platforms gain a competitive advantage. Datasets like Jobs, News Events, Technology, Key Customers/Connections, Products and Website Evolution are all being used to identify new opportunities and stay ahead of the game. These are available through API, Webhooks or Flat Files and can be accessed daily, weekly, monthly or quarterly.
Contact mateja@predictleads.com to dive deeper into more ways that company intelligence data can help enhance your use case.
InReach Ventures uses technology to help scale venture capital. They make investments in early stage startups throughout Europe. They built their own proprietary software and developed a new model of investing. This helps them discover and invest in the most promising startups.
Thereâs a few major data challenges VCâs often face. These include data quality and the time, effort, and cost it takes to acquire or crawl data.
Here is a short interview with Ben Smith, the Co-Founder / Partner / CTO of InReach Ventures. It explains how PredictLeads company intelligence data helps InReach Ventures. This assists them in discovering new companies and tracking growth signals for companies of interest.
How do you identify growing companies?
âInReach combines data from lots of different data sources. Some of that is around signals on how a company is performing like PredictLeads data. This helps us to find startups from all over Europe. This data, along with other types, allows us to look at how companies are growing. We can see whether theyâre growing their team, getting new customers, or forming new business connections. In addition, we see if they’re partnering with different companies. â
Venture capital growth driven by PredictLeads data
Are there any specifics on how PredictLeads data is being used?
âWith job postings in particular, outside the general idea that a company is growing positively, it gives us an idea whether there is real substance behind a company. Seeing that a company has a product and engineering DNA and are looking to invest more in it is a positive.”
What challenges were you able to overcome with PredictLeads data?
âItâs all about how best we leverage our own product and engineering resources. It involves the InReach team focusing on what weâre good at. Meanwhile, we work with partners that are better than us in certain areas. This is an important point of leverage.â
Why did you decide to subscribe to PredictLeads data?
âPredictLeads helped us by doing some of the work that we had always planned. However, we had never been able to prioritize it. They assist in finding news events around a particular company. Identifying company customers through logos/connections is really interesting for us. And, itâs something that takes significant time and effort to get right.”
What’s your view on the VC industry using data and what are the biggest challenges on the horizon in the industry?
âThe value of data, machine learning, and a data-driven approach to capital is an ever-growing trend. The point of venture capital is to fund innovation. However, how much innovation is happening in venture capital in the past 10 years is very limited. I think there is a change now. Data and software are being seen as a way for venture firms to innovate their model.
The issue that traditional VC firms first face is cultural. At their core, they are not a technology firm but a professional services organization. Where we think we have an advantage is that we started as a technology, product, and engineering organization. Thus, we take a very data-driven approach to venture capital. Thatâs where we think we will long term hold the advantage.
We started doing this earlier. Traditional venture capital will start to utilize data over time, but they are not tech or engineering organizations at their core. Short term, data and tech will play a broader role. This occurs as the whole industry starts using them. It’s becoming more of a buzz as data demand increases.â
What are some of the trends in Venture Capital?
“My co-founder and Investment Partner Roberto laid out the data trend in VC well in his blog post: The Full Stack Venture Capitalist“
How do you see PredictLeads to help you achieve your long term goals?
âTwo things PredictLeads does and will continue to do is help us discover that a startup exists in the first place. Then it tells us whether thereâs something interesting happening that we might want to talk to them about.â
